


import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
 import torch

if is_vision_available():
 import PIL


_SCREAMING_SNAKE_CASE     :					Any      =       logging.get_logger(__name__)
def      UpperCAmelCase__		(UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_							):



     """simple docstring"""






     def constraint_to_multiple_of(UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_=0				,UpperCamelCase_=None							):
          snake_case											=							round(val / multiple							) * multiple

          if max_val is not None and x > max_val:
               snake_case											=							math.floor(val / multiple							) * multiple

          if x < min_val:
               snake_case											=							math.ceil(val / multiple							) * multiple

          return x

     snake_case											=							(output_size, output_size) if isinstance(UpperCamelCase_				,UpperCamelCase_							) else output_size

     snake_case					,    snake_case											=							get_image_size(UpperCamelCase_							)
     snake_case					,    snake_case											=							output_size

     # determine new height and width
     snake_case											=							output_height / input_height
     snake_case											=							output_width / input_width

     if keep_aspect_ratio:
          # scale as little as possible
          if abs(1 - scale_width							) < abs(1 - scale_height							):
               # fit width
               snake_case											=							scale_width
          else:
               # fit height
               snake_case											=							scale_height

     snake_case											=							constraint_to_multiple_of(scale_height * input_height				,multiple=UpperCamelCase_							)
     snake_case											=							constraint_to_multiple_of(scale_width * input_width				,multiple=UpperCamelCase_							)

     return (new_height, new_width)

class     A__   (       snake_case__      ):







    """simple docstring"""

    __magic_name__         =						['pixel_values']
    def __init__(			self     ,   __snake_case = True     ,   __snake_case = None     ,   __snake_case = PILImageResampling.BILINEAR     ,   __snake_case = False     ,   __snake_case = 1     ,   __snake_case = True     ,   __snake_case = 1 / 2_5_5     ,   __snake_case = True     ,   __snake_case = None     ,   __snake_case = None     ,   **__snake_case     ,   ):
         super().__init__(**__snake_case       )
         snake_case											=							size if size is not None else {'''height''': 3_8_4, '''width''': 3_8_4}
         snake_case											=							get_size_dict(__snake_case       )
         snake_case											=							do_resize
         snake_case											=							size
         snake_case											=							keep_aspect_ratio
         snake_case											=							ensure_multiple_of
         snake_case											=							resample
         snake_case											=							do_rescale
         snake_case											=							rescale_factor
         snake_case											=							do_normalize
         snake_case											=							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         snake_case											=							image_std if image_std is not None else IMAGENET_STANDARD_STD
    def 						a_     (			self     ,   __snake_case     ,   __snake_case     ,   __snake_case = False     ,   __snake_case = 1     ,   __snake_case = PILImageResampling.BICUBIC     ,   __snake_case = None     ,   **__snake_case     ,   ):
         snake_case											=							get_size_dict(__snake_case       )
         if "height" not in size or "width" not in size:
              raise ValueError(F'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'''       )
         snake_case											=							get_resize_output_image_size(
             __snake_case     ,   output_size=(size['''height'''], size['''width'''])     ,   keep_aspect_ratio=__snake_case     ,   multiple=__snake_case     ,   )
         return resize(__snake_case     ,   size=__snake_case     ,   resample=__snake_case     ,   data_format=__snake_case     ,   **__snake_case       )
    def 						a_     (			self     ,   __snake_case     ,   __snake_case     ,   __snake_case = None     ,   **__snake_case     ,   ):
         return rescale(__snake_case     ,   scale=__snake_case     ,   data_format=__snake_case     ,   **__snake_case       )
    def 						a_     (			self     ,   __snake_case     ,   __snake_case     ,   __snake_case     ,   __snake_case = None     ,   **__snake_case     ,   ):
         return normalize(__snake_case     ,   mean=__snake_case     ,   std=__snake_case     ,   data_format=__snake_case     ,   **__snake_case       )
    def 						a_     (			self     ,   __snake_case     ,   __snake_case = None     ,   __snake_case = None     ,   __snake_case = None     ,   __snake_case = None     ,   __snake_case = None     ,   __snake_case = None     ,   __snake_case = None     ,   __snake_case = None     ,   __snake_case = None     ,   __snake_case = None     ,   __snake_case = None     ,   __snake_case = ChannelDimension.FIRST     ,   **__snake_case     ,   ):
         snake_case											=							do_resize if do_resize is not None else self.do_resize
         snake_case											=							size if size is not None else self.size
         snake_case											=							get_size_dict(__snake_case       )
         snake_case											=							keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
         snake_case											=							ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
         snake_case											=							resample if resample is not None else self.resample
         snake_case											=							do_rescale if do_rescale is not None else self.do_rescale
         snake_case											=							rescale_factor if rescale_factor is not None else self.rescale_factor
         snake_case											=							do_normalize if do_normalize is not None else self.do_normalize
         snake_case											=							image_mean if image_mean is not None else self.image_mean
         snake_case											=							image_std if image_std is not None else self.image_std

         snake_case											=							make_list_of_images(__snake_case       )

         if not valid_images(__snake_case       ):
              raise ValueError(
                  '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                  '''torch.Tensor, tf.Tensor or jax.ndarray.'''       )

         if do_resize and size is None or resample is None:
              raise ValueError('''Size and resample must be specified if do_resize is True.'''       )

         if do_rescale and rescale_factor is None:
              raise ValueError('''Rescale factor must be specified if do_rescale is True.'''       )

         if do_normalize and (image_mean is None or image_std is None):
              raise ValueError('''Image mean and std must be specified if do_normalize is True.'''       )

         # All transformations expect numpy arrays.
         snake_case											=							[to_numpy_array(__snake_case       ) for image in images]

         if do_resize:
              snake_case											=							[self.resize(image=__snake_case     ,   size=__snake_case     ,   resample=__snake_case       ) for image in images]

         if do_rescale:
              snake_case											=							[self.rescale(image=__snake_case     ,   scale=__snake_case       ) for image in images]

         if do_normalize:
              snake_case											=							[self.normalize(image=__snake_case     ,   mean=__snake_case     ,   std=__snake_case       ) for image in images]

         snake_case											=							[to_channel_dimension_format(__snake_case     ,   __snake_case       ) for image in images]

         snake_case											=							{'''pixel_values''': images}
         return BatchFeature(data=__snake_case     ,   tensor_type=__snake_case       )




    def 						a_     (			self     ,   __snake_case     ,   __snake_case = None       ):
         snake_case											=							outputs.logits

         # Resize logits and compute semantic segmentation maps
         if target_sizes is not None:
              if len(__snake_case       ) != len(__snake_case       ):
                   raise ValueError(
                       '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''       )

              if is_torch_tensor(__snake_case       ):
                   snake_case											=							target_sizes.numpy()

              snake_case											=							[]

              for idx in range(len(__snake_case       )       ):
                   snake_case											=							torch.nn.functional.interpolate(
                       logits[idx].unsqueeze(dim=0       )     ,   size=target_sizes[idx]     ,   mode='''bilinear'''     ,   align_corners=__snake_case       )
                   snake_case											=							resized_logits[0].argmax(dim=0       )
                   semantic_segmentation.append(__snake_case       )
         else:
              snake_case											=							logits.argmax(dim=1       )
              snake_case											=							[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]       )]

         return semantic_segmentation








import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch


if is_vision_available():
 from transformers import TvltImageProcessor

if is_speech_available():
 from transformers import TvltFeatureExtractor

from transformers import TvltProcessor

@require_torch
class     A__   (       unittest.TestCase      ):







    """simple docstring"""

    def 						a_     (			self       ):
         snake_case											=							'''ZinengTang/tvlt-base'''
         snake_case											=							tempfile.mkdtemp()
    def 						a_     (			self     ,   **__snake_case       ):
         return TvltImageProcessor.from_pretrained(self.checkpoint     ,   **__snake_case       )
    def 						a_     (			self     ,   **__snake_case       ):
         return TvltFeatureExtractor.from_pretrained(self.checkpoint     ,   **__snake_case       )
    def 						a_     (			self       ):
         shutil.rmtree(self.tmpdirname       )
    def 						a_     (			self       ):
         snake_case											=							self.get_image_processor()
         snake_case											=							self.get_feature_extractor()

         snake_case											=							TvltProcessor(image_processor=__snake_case     ,   feature_extractor=__snake_case       )
         processor.save_pretrained(self.tmpdirname       )
         snake_case											=							TvltProcessor.from_pretrained(self.tmpdirname       )

         self.assertIsInstance(processor.feature_extractor     ,   __snake_case       )
         self.assertIsInstance(processor.image_processor     ,   __snake_case       )
    def 						a_     (			self       ):
         snake_case											=							self.get_image_processor()
         snake_case											=							self.get_feature_extractor()

         snake_case											=							TvltProcessor(image_processor=__snake_case     ,   feature_extractor=__snake_case       )

         snake_case											=							np.ones([1_2_0_0_0]       )

         snake_case											=							feature_extractor(__snake_case     ,   return_tensors='''np'''       )
         snake_case											=							processor(audio=__snake_case     ,   return_tensors='''np'''       )

         for key in audio_dict.keys():
              self.assertAlmostEqual(audio_dict[key].sum()     ,   input_processor[key].sum()     ,   delta=1E-2       )
    def 						a_     (			self       ):
         snake_case											=							self.get_image_processor()
         snake_case											=							self.get_feature_extractor()

         snake_case											=							TvltProcessor(image_processor=__snake_case     ,   feature_extractor=__snake_case       )

         snake_case											=							np.ones([3, 2_2_4, 2_2_4]       )

         snake_case											=							image_processor(__snake_case     ,   return_tensors='''np'''       )
         snake_case											=							processor(images=__snake_case     ,   return_tensors='''np'''       )

         for key in image_dict.keys():
              self.assertAlmostEqual(image_dict[key].sum()     ,   input_processor[key].sum()     ,   delta=1E-2       )
    def 						a_     (			self       ):
         snake_case											=							self.get_image_processor()
         snake_case											=							self.get_feature_extractor()

         snake_case											=							TvltProcessor(image_processor=__snake_case     ,   feature_extractor=__snake_case       )

         snake_case											=							np.ones([1_2_0_0_0]       )
         snake_case											=							np.ones([3, 2_2_4, 2_2_4]       )

         snake_case											=							processor(audio=__snake_case     ,   images=__snake_case       )

         self.assertListEqual(list(inputs.keys()       )     ,   ['''audio_values''', '''audio_mask''', '''pixel_values''', '''pixel_mask''']       )

         # test if it raises when no input is passed
         with pytest.raises(__snake_case       ):
              processor()




    def 						a_     (			self       ):
         snake_case											=							self.get_image_processor()
         snake_case											=							self.get_feature_extractor()

         snake_case											=							TvltProcessor(image_processor=__snake_case     ,   feature_extractor=__snake_case       )

         self.assertListEqual(
             processor.model_input_names     ,   image_processor.model_input_names + feature_extractor.model_input_names     ,   msg='''`processor` and `image_processor`+`feature_extractor` model input names do not match'''     ,   )




